SoroushVahidi/scidocs huggingface dataset:
Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques
(2017)

Duplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80%, which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.
Insights from Price's equation into evolutionary epidemiology
(2006)

The basic reproduction number, denoted by R0, is one of the most important quantities in epidemiological theory ([11], [23]). It is defined as the expected number of new infections generated by an infected individual in an otherwise wholly susceptible population ([2], [12], [23]). Part of the reason why R0 plays such a central role in this body of theory undoubtedly stems from its relatively simple and intuitively sensible interpretation as a measure of pathogen reproduction. If R0 is less than unity then we expect the pathogen to die out since each infected individual fails to generate at least one other infection during the lifetime of the infection. Given that R0 is a measure of pathogen reproductive success, it is not surprising that this quantity has also come to form the basis of most evolutionary considerations of host-pathogen interactions ([1], [18]). For example, mathematical models for numerous epidemiological settings have been used to demonstrate that natural selection is often expected to favour the pathogen strain that results in the largest value of R0 ([6], [18]). In more complex epidemiological settings such optimization criteria typically cannot be derived and instead a game-theoretic approach is taken ([5]). In this context a measure of the fitness of a rare mutant pathogen strain is used to characterize the evolutionarily stable strain (i.e., the strain that, if present within the population in sufficient numbers, cannot be displaced by any mutant strain that arises). Typically R0 again plays a central role as the measure of mutant fitness in such invasion analyses ([10], [18], [30]). In this chapter we consider an alternative approach for developing theory in evolutionary epidemiology. Rather than using the total number of new infections generated by an infected individual (i.e., R0) as a measure of pathogen fitness we use the instantaneous rate of change of the number of infected hosts instead (see also [3], [18]). This shifts the focus from a consideration of pathogen reproductive success per generation to pathogen reproductive success per unit time. One very useful result of this change in focus is that we can then model the time dynamics of evolutionary change in the pathogen population simultaneously with the epidemiological dynamics, rather than simply characterizing the evolutionary equilibria that are expected. Even more importantly, however, this seemingly slight change
Context Prediction for Unsupervised Deep Learning on Point Clouds
(2019)

Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation. While massive point cloud datasets can be captured using modern scanning technology, manually labelling such large 3D point clouds for supervised learning tasks is a cumbersome process. This necessitates effective unsupervised learning methods that can produce representations such that downstream tasks require significantly fewer annotated samples. We propose a novel method for unsupervised learning on raw point cloud data in which a neural network is trained to predict the spatial relationship between two point cloud segments. While solving this task, representations that capture semantic properties of the point cloud are learned. Our method outperforms previous unsupervised learning approaches in downstream object classification and segmentation tasks and performs on par with fully supervised methods.
Scatterplots: Tasks, Data, and Designs
(2018)

Traditional scatterplots fail to scale as the complexity and amount of data increases. In response, there exist many design options that modify or expand the traditional scatterplot design to meet these larger scales. This breadth of design options creates challenges for designers and practitioners who must select appropriate designs for particular analysis goals. In this paper, we help designers in making design choices for scatterplot visualizations. We survey the literature to catalog scatterplot-specific analysis tasks. We look at how data characteristics influence design decisions. We then survey scatterplot-like designs to understand the range of design options. Building upon these three organizations, we connect data characteristics, analysis tasks, and design choices in order to generate challenges, open questions, and example best practices for the effective design of scatterplots.
The design of scaffolds for use in tissue engineering. Part I. Traditional factors.
(2001)

In tissue engineering, a highly porous artificial extracellular matrix or scaffold is required to accommodate mammalian cells and guide their growth and tissue regeneration in three dimensions. However, existing three-dimensional scaffolds for tissue engineering proved less than ideal for actual applications, not only because they lack mechanical strength, but they also do not guarantee interconnected channels. In this paper, the authors analyze the factors necessary to enhance the design and manufacture of scaffolds for use in tissue engineering in terms of materials, structure, and mechanical properties and review the traditional scaffold fabrication methods. Advantages and limitations of these traditional methods are also discussed.
From Polynesian healers to health food stores: changing perspectives of Morinda citrifolia (Rubiaceae).
(2002)

Morinda citrifolia L (noni) is one of the most important traditional Polynesian medicinal plants. Remedies from isolated Polynesian cultures, such as that of Rotuma, illustrate traditional indications that focus upon leaves, roots, bark, and green fruit, primarily for topical ailments. Anecdotally collected Hawaiian remedies that employ noni fruit illustrate changing usage patterns with shifts in recent times to preparation of juice made of ripe or decaying fruit. Ralph M. Heinicke promoted a wide range of claims about noni, and these seem to have fueled much of the current commercial interest in the plant. Recent studies of the proliferation of commercial products have shown that noni product manufacturers are promoting a range of therapeutic claims. These claims are based upon traditional Polynesian uses, Heinicke's ideas, and fragments of recent scientific studies including the activity of noni in the treatment of cancer. A review is provided of recent studies of potential anticancer activity of noni fruit. While noni's anticancer potential is still being explored, it continues to be widely used by Polynesians and non-Polynesians alike for both traditional and newly hypothesized indications.
How Alluring Are Dark Personalities ? The Dark Triad and Attractiveness in Speed Dating
(2016)

Copy Abstract: Dark Triad traits (narcissism, psychopathy, and Machiavellianism) are linked to the pursuit of short-term mating strategies, but they may have differential effects on actual mating success in naturalistic scenarios: Narcissism may be a facilitator for men’s short-term mating success, while Machiavellianism and psychopathy may be detrimental. To date, little is known about the attractiveness of Dark Triad traits in women. In a speed-dating study, we assessed participants’ Dark Triad traits, Big Five personality traits, and physical attractiveness in N=90 heterosexual individuals (46 women and 44 men). Each participant rated each partner’s mate appeal for shortand long-term relationships. Across both sexes, narcissism was positively associated with mate appeal for shortand long-term relationships. Further analyses indicated that these associations were due to the shared variance among narcissism and extraversion in men and narcissism and physical attractiveness in women, respectively. In women, psychopathy was also positively associated with mate appeal for short-term relationships. Regarding mating preferences, narcissism was found to involve greater choosiness in the rating of others’ mate appeal (but not actual choices) in men, while psychopathy was associated with greater openness towards short-term relationships in women. Copyright © 2016 European Association of Personality Psychology
Evaluating Sentiment Analysis in the Context of Securities Trading
(2016)

There are numerous studies suggesting that published news stories have an important effect on the direction of the stock market, its volatility, the volume of trades, and the value of individual stocks mentioned in the news. There is even some published research suggesting that automated sentiment analysis of news documents, quarterly reports, blogs and/or twitter data can be productively used as part of a trading strategy. This paper presents just such a family of trading strategies, and then uses this application to re-examine some of the tacit assumptions behind how sentiment analyzers are generally evaluated, in spite of the contexts of their application. This discrepancy comes at a cost.
Feature selection methods for conversational recommender systems
(2005)

This paper focuses on question selection methods for conversational recommender systems. We consider a scenario, where given an initial user query, the recommender system may ask the user to provide additional features describing the searched products. The objective is to generate questions/features that a user would likely reply, and if replied, would effectively reduce the result size of the initial query. Classical entropy-based feature selection methods are effective in term of result size reduction, but they select questions uncorrelated with user needs and therefore unlikely to be replied. We propose two feature-selection methods that combine feature entropy with an appropriate measure of feature relevance. We evaluated these methods in a set of simulated interactions where a probabilistic model of user behavior is exploited. The results show that these methods outperform entropy-based feature selection.
Lazy Decision Trees
(1996)

Lazy learning algorithms, exemplified by nearestneighbor algorithms, do not induce a concise hypothesis from a given training set; the inductive process is delayed until a test instance is given. Algorithms for constructing decision trees, such as C4.5, ID3, and CART create a single “best” decision tree during the training phase, and this tree is then used to classify test instances. The tests at the nodes of the constructed tree are good on average, but there may be better tests for classifying a specific instance. We propose a lazy decision tree algorithm-LAzuDT-that conceptually constructs the “best” decision tree for each test instance. In practice, only a path needs to be constructed, and a caching scheme makes the algorithm fast. The algorithm is robust with respect to missing values without resorting to the complicated methods usually seen in induction of decision trees. Experiments on real and artificial problems are presented.
A consensus-based approach for platooning with inter-vehicular communications
(2015)

Automated and coordinated vehicles' driving (platooning) is gaining more and more attention today and it represents a challenging scenario heavily relying on wireless Inter-Vehicular Communication (IVC). In this paper, we propose a novel controller for vehicle platooning based on consensus. Opposed to current approaches where the logical control topology is fixed a priori and the control law designed consequently, we design a system whose control topology can be reconfigured depending on the actual network status. Moreover, the controller does not require the vehicles to be radar equipped and automatically compensates outdated information caused by network delays. We define the control law and analyze it in both analytical and simulative way, showing its robustness in different network scenarios. We consider three different wireless network settings: uncorrelated Bernoullian losses, correlated losses using a Gilbert-Elliott channel, and a realistic traffic scenario with interferences caused by other vehicles. Finally, we compare our strategy with another state of the art controller. The results show the ability of the proposed approach to maintain a stable string of vehicles even in the presence of strong interference, delays, and fading conditions, providing higher comfort and safety for platoon drivers.
Thinking Positively - Explanatory Feedback for Conversational Recommender Systems
(2004)

When it comes to buying expensive goods people expect to be skillfully steered through the options by well-informed sales assistants that are capable of balancing the user’s many and varied requirements. In addition users often need to be educated about the product-space, especially if they are to come to understand what is available and why certain options are being recommended by the sales-assistant. The same issues arise in interactive recommender systems, our online equivalent of a sales assistant and explanation in recommender systems, as a means to educate users and justify recommendations, is now well accepted. In this paper we focus on a novel approach to explanation. Instead of attempting to justify a particular recommendation we focus on how explanations can help users to understand the recommendation opportunities that remain if the current recommendation should not meet their requirements. We describe how this approach to explanation is tightly coupled with the generation of compound critiques, which act as a form of feedback for the users. And we argue that these explanation-rich critiques have the potential to dramatically improve recommender performance and usability.
A Survey of Definitions and Models of Exploratory Search
(2017)

Exploratory search has an unclear and open-ended definition. The complexity of the task and the difficulty of defining this activity are reflected in the limits of existing evaluation methods for exploratory search systems. In order to improve them, we intend to design an evaluation method based on a user-centered model of exploratory search. In this work, we identified and defined the characteristics of exploratory search and used them as an information seeking model evaluation grid. We tested this analytic grid on two information seeking models: Ellis' and Marchionini's models. The results show that Marchonini's model does not match our evaluation method's requirements whereas on the other hand Ellis' model could be adapted to better suit exploratory search.
Efficient FIR Filter Design Using Modified Carry Select Adder & Wallace Tree Multiplier
(2013)

An area-power-delay efficient design of FIR filter is described in this paper. In proposed multiplier unit high speed is achieved using XOR-XNOR column by column reduction compressors instead of compressors using full adder. The carry propagation delay and area of carry select adder is reduced by splitting carry select adder into equal bit groups. The proposed carry select adder unit consumes less power than conventional carry select adder unit. With proposed multiplier unit and carry select adder unit, the designed FIR Filter consumes 55% power less than the conventional filter without significant increase in area. The power & delay comparison is performed for both existing and proposed method of FIR filter. The design is implemented using 0.18μm technology. Index Terms FIR filter design, FDA Tool, Low Power VLSI, Wallace tree multiplication.
An efficient and generic reversible debugger using the virtual machine based approach
(2005)

The reverse execution of programs is a function where programs are executed backward in time. A reversible debugger is a debugger that provides such a functionality. In this paper, we propose a novel reversible debugger that enables reverse execution of programs written in the C language. Our approach takes the virtual machine based approach. In this approach, the target program is executed on a special virtual machine. Our contribution in this paper is two-fold. First, we propose an approach that can address problems of (1) compatibility and (2) efficiency that exist in previous works. By compatibility, we mean that previous debuggers are not generic, i.e., they support only a special language or special intermediate code. Second, our approach provides two execution modes: the native mode, where the debuggee is directly executed on a real CPU, and the virtual machine mode, where the debuggee is executed on a virtual machine. Currently, our debugger provides four types of trade-off settings (designated by unit and optimization) to consider trade-offs between granularity, accuracy, overhead and memory requirement. The user can choose the appropriate setting flexibly during debugging without finishing and restarting the debuggee.
Real-time Empirical Mode Decomposition for EEG signal enhancement
(2013)

Electroencephalography (EEG) recordings are used for brain research. However, in most cases, the recordings not only contain brain waves, but also artifacts of physiological or technical origins. A recent approach used for signal enhancement is Empirical Mode Decomposition (EMD), an adaptive data-driven technique which decomposes non-stationary data into so-called Intrinsic Mode Functions (IMFs). Once the IMFs are obtained, they can be used for denoising and detrending purposes. This paper presents a real-time implementation of an EMD-based signal enhancement scheme. The proposed implementation is used for removing noise, for suppressing muscle artifacts, and for detrending EEG signals in an automatic manner and in real-time. The proposed algorithm is demonstrated by application to a simulated and a real EEG data set from an epilepsy patient. Moreover, by visual inspection and in a quantitative manner, it is shown that after the EMD in real-time, the EEG signals are enhanced.
Span, CRUNCH, and Beyond: Working Memory Capacity and the Aging Brain
(2010)

Neuroimaging data emphasize that older adults often show greater extent of brain activation than younger adults for similar objective levels of difficulty. A possible interpretation of this finding is that older adults need to recruit neuronal resources at lower loads than younger adults, leaving no resources for higher loads, and thus leading to performance decrements [Compensation-Related Utilization of Neural Circuits Hypothesis; e.g., Reuter-Lorenz, P. A., & Cappell, K. A. Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science, 17, 177–182, 2008]. The Compensation-Related Utilization of Neural Circuits Hypothesis leads to the prediction that activation differences between younger and older adults should disappear when task difficulty is made subjectively comparable. In a Sternberg memory search task, this can be achieved by assessing brain activity as a function of load relative to the individual's memory span, which declines with age. Specifically, we hypothesized a nonlinear relationship between load and both performance and brain activity and predicted that asymptotes in the brain activation function should correlate with performance asymptotes (corresponding to working memory span). The results suggest that age differences in brain activation can be largely attributed to individual variations in working memory span. Interestingly, the brain activation data show a sigmoid relationship with load. Results are discussed in terms of Cowan's [Cowan, N. The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–114, 2001] model of working memory and theories of impaired inhibitory processes in aging.
Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces
(2012)

Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.
ECG measurement on a chair without conductive contact
(2006)

For the purpose of long-term, everyday electrocardiogram (ECG) monitoring, we present a convenient method of ECG measurement without direct conductive contact with the skin while subjects sat on a chair wearing normal clothes. Measurements were made using electrodes attached to the back of a chair, high-input-impedance amplifiers mounted on the electrodes, and a large ground-plane placed on the chair seat. ECGs were obtained by the presented method for several types of clothing and compared to ECGs obtained from conventional measurement using Ag-AgCl electrodes. Motion artifacts caused by usual desk works were investigated. This study shows the feasibility of the method for long-term, convenient, everyday use